AI is changing how we work.
But adoption is still deeply human.

Start by learning enough to ask better questions, make sensible decisions and guide your people through the change. You do not need to become a technical expert. Begin with a few practical uses in your own work, such as thinking, planning, communication, summarising or problem-solving, and build confidence through use.

AI adoption should not sit with one person or department. The CEO or MD sponsors the direction and sets the tone. IT guides security, systems and technical risk. People and learning teams support confidence and change. Department leaders identify useful applications in real workflows. A small cross-functional team with clear executive sponsorship is often the strongest starting point.

Start small, practical and human. Choose a few use cases that make real work easier, create space for basic questions and guided practice, and explain what responsible use looks like. Treat AI as a learning journey, not a sudden technical transformation. Confidence grows when people can see relevance and feel safe enough to experiment.

Yes. AI changes how people think, work, communicate and make decisions. It can also raise concerns about competence, trust and job security. Change management helps leaders communicate clearly, involve people appropriately, establish practical guardrails and support new behaviours over time. AI implementation is both a technology initiative and a people-change process.

An AI Confidence Booster is a practical, plain-language workshop for leaders, teams and professionals. It may include demonstrations, guided prompting, workplace examples, responsible-use discussions and applications for communication, planning, problem-solving and decision-making. The aim is not technical mastery. It is to help people feel less intimidated and more capable of using AI thoughtfully.

Yes. A facilitated leadership session creates space to align on the opportunity, risks, business priorities, people impact, responsible use and practical next steps. It is particularly useful when some leaders are enthusiastic, others are cautious and the organisation does not yet share a clear view of what AI adoption should mean.

Bring the conversation into the open. Ask which tools people use, for what work, what information they enter and what useful practices early adopters have discovered. Then create practical guidelines that clarify what is allowed, what needs approval and what must never be shared. A blanket shutdown can drive use underground. Clear guardrails can protect the organisation while preserving curiosity and initiative.

Not necessarily, but the starting point must be realistic. Do not add every AI tool at once. Identify where people already struggle and introduce one or two uses that make work simpler, such as clearer emails, meeting preparation or summarising information. In some teams, better use of existing tools should come first. The goal is to reduce overwhelm and build capability in manageable steps.

Mel focuses on human-centred AI adoption rather than coding or technical implementation. Her work combines practical AI learning with business coaching, facilitation and change management. This is useful for organisations that need more than tool demonstrations: they need confidence, leadership alignment, responsible use, practical integration and attention to what happens when humans have to work differently.

An AI adoption consultant helps an organisation move from having access to AI technology to actually using it effectively. This can involve strategy, workflows, employee confidence, training, change management, governance, communication, leadership alignment and practical use cases.

People may resist AI because of uncertainty, fear of mistakes or job loss, weak use cases, unclear policies, insufficient training or confusion about approved tools. What looks like resistance is often a lack of confidence or clarity.

AI confidence is a person’s ability and willingness to engage with AI with enough understanding, permission and judgement to experiment and use it appropriately. It does not mean knowing everything. It means having enough clarity and support to begin thoughtfully.

Human-centred AI adoption considers how people, workflows, culture, leadership and technology interact. It focuses not only on what the technology can do, but on how people understand, trust, use and adapt to it.

Humaning with AI is Mel Tomlinson’s philosophy for working with artificial intelligence while continuing to develop curiosity, judgement, critical thinking, creativity and other distinctly human capabilities.

Start with clear use cases, practical training, understandable guardrails and permission to experiment responsibly. Involve leaders, address workflow changes and build confidence instead of simply providing access to tools.

Shadow AI, or Bring Your Own AI, refers to employees using AI tools independently, sometimes without formal approval or oversight. It often emerges when official tools, policies, guidance or workflows have not kept pace with how people want to work.

An AI champion supports responsible adoption inside an organisation. Effective champions help colleagues learn, experiment, share use cases, identify barriers and connect employee experience with the wider AI strategy.

IT is vital for technology, security, integration and governance. Effective adoption, however, also requires leadership, people functions, change, operations, learning, risk and the employees whose workflows are changing.

Yes. Mel works with leaders, teams and organisations on practical AI learning, AI confidence and the human side of adoption. She is based in Cape Town and works with clients in South Africa and internationally.